EEG analysis on skull conductivity perturbations using realistic head model

Bashar, Md. Rezaul and Li, Yan and Wen, Peng (2009) EEG analysis on skull conductivity perturbations using realistic head model. In: RSKT 2009: 4th International Conference on Rough Set and Knowledge Technology , 14-16 Jul 2009, Gold Coast, Australia.

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Abstract

Measurement of electroencephalogram (EEG) requires accurate estimation of tissue conductivity. Among the head tissues, skull compartment has less conductivity due to compacta and spongiosa, which impacts on EEG measurement. Therefore, skull conductivity plays a vital role in head modeling, forward computation and source localization. In this study, we have investigated the effects of scalp potentials due to skull conductivity perturbations in realistic head models using different skull to brain and/or scalp conductivity ratio (σ ratio). Several studies used this σ ratio as 1/80, however, other studies found the values of σ ratio between 1/20 and 1/72. Each head model constructed from the values of different σ ratio ranging from 1/20 to 1/72 is compared to the head model constructed from σ ratio = 1/80. The obtained results demonstrated that the skull conductivity perturbations have effects on EEG and the head model constructed from less σ ratio generates larger errors due to higher potential differences.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Series: Lecture Notes in Computer Science, v. 5589. Print copy held USQ Library 006.3 Rou. This is the authors' version of the work. It is posted here with permission of the publisher for your personal use. No further distribution is permitted.
Depositing User: Dr Yan Li
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 24 Jan 2010 02:42
Last Modified: 02 Jul 2013 23:36
Uncontrolled Keywords: EEG; head modelling; anisotropic conductivity; MRI and FEM; electroencephalography
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
11 Medical and Health Sciences > 1116 Medical Physiology > 111603 Systems Physiology
11 Medical and Health Sciences > 1109 Neurosciences > 110999 Neurosciences not elsewhere classified
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: doi: 10.1007/978-3-642-02962-2_26
URI: http://eprints.usq.edu.au/id/eprint/6647

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